"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP servers:

A minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.

Enables AI-powered generation, customization, and documentation of its-just-ui React components. Provides tools for component generation, theme management, form creation, responsive layouts, and accessibility guidance.

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

Enables AI agents to drive spec-driven development workflows from the IDE by exposing tools for exploring, proposing, applying, verifying, archiving, listing, showing, validating, status, and context operations.

Enables local video inspection and offline transcription with timestamped JSON and Markdown output, enforced filesystem boundaries, and reproducible content-addressed caching.

Provides live wait times and office details for all 60 Oregon DMV field offices by scraping the ODOT website, with no authentication required.

Enables AI coding agents to orchestrate the full software development lifecycle on GitHub, including planning, issue creation, code review, security triage, and release readiness checks.

Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.

A local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.

Provides a Model Context Protocol server with tools for knowledge base search, retrieval, and addition, plus system time, resources, and prompts. Supports stdio and Streamable HTTP transports for local and shared deployments.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Read and write email through the Atomic Mail from an AI agent. Handles proof-of-work authentication and JMAP so the agent thinks in JMAP method calls. Use when the user asks to register an email inbox, list mailboxes, fetch or send email.

A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.

Enables coding agents to retrieve project context from an AnythingLLM RAG at session start and write back human-approved durable facts via five workflow-shaped tools.

Acts as the 'Hands and Eyes' for an Autonomous AI Agent, bridging Large Language Models and your local development environment to enable safe file manipulation, context reading, command execution, and documentation verification.

An MCP (Model Context Protocol) server that enables interactive debugging with code instrumentation for AI coding assistants. Inspired by Cursor's debug mode.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

Provides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.

MCP server for searching, browsing, and analyzing decompiled Minecraft source code locally. Supports symbol lookup, text search, reference lookup, and lightweight RAG.